On y voit rien. Le recours aux sources de presse pour l'analyse des mobilisations protestataires

Aujourd'hui la PEA est devenue la méthode la plus employée dans le champ des études de mobilisation. Toutefois, il est frappant que les limites, nombreuses, de cette méthode, demeurent peu explorées, malgré une abondante littérature critique. C'est à ces questions des biais propres aux sources de presse dans le cadre de la PEA et aux moyens de les explorer que ce working paper se consacre. Nous commencerons par montrer, en deux temps, comment la question de la systématicité gagne à être explorée à partir d'enquêtes qualitatives, en nous appuyant sur un travail inédit mené naguère auprès du journal Le Monde, dans le cadre d'une recherche collective sur les transformations de l'activisme environnemental en Europe, dont les données seront comparées avec des sources de police, le dépouillement des dépêches de l'Agence France-Presse sur six mois, et surtout une série d'interviews avec des journalistes spécialisés dans l'environnement. L'on revient dans un second temps à la question de la sélectivité des sources en tentant de montrer comment dans toute une série de circonstances et pour toute une série de groupes, les luttes ne sauraient se réduire ni aux événements protestataires ni à un appel à l'Etat ou à l'opinion. L'on espère ainsi proposer des moyens de se garder d'un défaut commun à tout conventionnalisme méthodologique qui, ici comme ailleurs, frappe la recherche, soit la réitération de mesures conventionnelles ayant pour effet de les institutionnaliser en dehors de toute réflexion sur leur pertinence. Protest Events Analysis (PEA) has by no doubts become one the most used method in the field of social movement research. Yet, the numerous biases of that method have not been explored completely, despite a rich litterature. Our paper first proposes a discussion of the systematicity of biases, based on an empirical research on environmental journalists in various French newspapers and Agence France press. Secondly, we deal with the question of selectivity of biases. Finally, we propose some methodological recommendations in order to improve our understanding of social movements and to avoid methodological conventionalism.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

The Role of Information in Contemporary Migration. More Sources but Less Informed

La migració internacional contemporània és integrada en un procés d'interconnexió global definit per les revolucions del transport i de les tecnologies de la informació i la comunicació. Una de les conseqüències d'aquesta interconnexió global és que les persones migrants tenen més capacitat per a processar informació tant abans com després de marxar. Aquests canvis podrien tenir implicacions inesperades per a la migració contemporània pel que fa a la capacitat de les persones migrants per a prendre decisions més informades, la reducció de la incertesa en contextos migratoris, el desdibuixament del concepte de distància o la decisió d'emigrar cap a llocs més llunyans. Aquesta recerca és important, ja que la manca de coneixement sobre aquesta qüestió podria contribuir a fer augmentar la distància entre els objectius de les polítiques de migració i els seus resultats. El paper que tenen els agents de la informació en els contextos migratoris també podria canviar. En aquest escenari, perquè les polítiques de migració siguin més efectives, s'haurà de tenir en compte la major capacitat de la població migrant de processar la informació i les fonts d'informació en què es confia. Aquest article demostra que l'equació més informació equival a més ben informat no es compleix sempre. Fins i tot en l'era de la informació, les fonts no fiables, les expectatives falses, la sobreinformació i els rumors encara són presents en els contextos migratoris. Tanmateix, defensem l'argument que aquests efectes no volguts es podrien reduir complint quatre requisits de la informació fiable: que sigui exhaustiva, que sigui rellevant, que s'hi confiï i que sigui actualitzada.

Independent sources of condition dependency and multiple pathways determine a composite trait: lessons from carotenoid-based plumage colouration.

Many colour ornaments are composite traits consisting of at least four components, which themselves may be more complex, determined by independent evolutionary pathways, and potentially being under different environmental control. To date, little evidence exists that several different components of colour elaboration are condition dependent and no direct evidence exists that different ornamental components are affected by different sources of variation. For example, in carotenoid-based plumage colouration, one of the best-known condition-dependent ornaments, colour elaboration stems from both condition-dependent pigment concentration and structural components. Some environmental flexibility of these components has been suggested, but specifically which and how they are affected remains unknown. Here, we tested whether multiple colour components may be condition dependent, by using a comprehensive 3 × 2 experimental design, in which we carotenoid supplemented and immune challenged great tit nestlings (Parus major) and quantified effects on different components of colouration. Plumage colouration was affected by an interaction between carotenoid availability and immune challenge. Path analyses showed that carotenoid supplementation increased plumage saturation via feather carotenoid concentration and via mechanisms unrelated to carotenoid deposition, while immune challenge affected feather length, but not carotenoid concentration. Thus, independent condition-dependent pathways, affected by different sources of variation, determine colour elaboration. This provides opportunities for the evolution of multiple signals within components of ornamental traits. This finding indicates that the selective forces shaping the evolution of different components of a composite trait and the trait's signal content may be more complex than believed so far, and that holistic approaches are required for drawing comprehensive evolutionary conclusions.

Explaining rapid transitions in the practice of flood risk management

This article draws on empirical material to reflect on what drives rapid change in flood risk management practice, reflecting wider interest in the way that scientific practices make risk landscapes and a specific focus on extreme events as drivers of rapid change. Such events are commonly referred to as a form of creative destruction, ones that reveal both the composition of socioenvironmental assemblages and provide a creative opportunity to remake those assemblages in alternate ways, therefore rapidly changing policy and practice. Drawing on wider thinking in complexity theory, we argue that what happens between events might be as, if not more, important than the events themselves. We use two empirical examples concerned with flood risk management practice: a rapid shift in the dominant technologies used to map flood risk in the United Kingdom and an experimental approach to public participation tested in two different locations, with dramatically different consequences. Both show that the state of the socioenvironmental assemblage in which the events take place matters as much as the magnitude of the events themselves. The periods between rapid changes are not simply periods of discursive consolidation but involve the ongoing mutation of such assemblages, which could either sensitize or desensitize them to rapid change. Understanding these intervening periods matters as much as the events themselves. If events matter, it is because of the ways in which they might bring into sharp focus the coding or framing of a socioenvironmental assemblage in policy or scientific practice irrespective of whether or not those events evolve the assemblage in subtle or more radical ways.

Fruits of Butia capitata (Mart.) Becc as good sources of β -carotene and provitamina

Butia capitata is a palm tree, widely found in the Brazilian savanna. Their fruits are largely used by local communities to prepare juices, jellies and ice-creams. The main objective of this work was to determine the carotenoids profile of Butia capitata fruits and their provitamin A values. Total carotenoids content ranged from 11.1 to 43.9 μg.g-1; β-carotene was the predominant carotenoid (5.2-22.8 μg.g-1), followed by γ-carotene, phytoene, phytofluene, ζ-carotene, α-cryptoxanthin (or zeinoxanthin) and α-carotene. Provitamin A values varied from 50 to 200 RAE.100g-1. This result suggests that B. capitata pulp may be a good source of β-carotene and provitamin A.

Application of nitrogen sources on grapevines and effect on yield and must composition

This study was carried out to evaluate the yield, total N content in leaves and must composition of grapes from the Cabernet Sauvignon variety subjected to the application of urea and organic compost. Cabernet Sauvignon grapevines in Rosário do Sul, RS, Brazil, in 2008, 2009 and 2010 were subjected to annual application of 40 kg N ha-1 in the form of organic compost and urea, and compared to unfertilized grapevines. In the 2008/09, 2009/10 and 2010/11 crop seasons, leaves were collected for analysis of total N content. At maturation of the grapes, the yield and quality attributes of the must were evaluated. The application of N sources, especially organic compost, increased the N content in the whole leaf at full flowering. Application of organic compost and urea has little effect on grape yield and does not affect the total nutrient content in the must, nor the enological attributes.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.